Evidence map›Paper›PMID 40833558›Full record

ArticleAging clinical and experimental research2025

Multimorbidity patterns and mortality in older adults: a two-cohort pooled analysis.

Cecilia Damiano, Simona Costanzo, Benedetta Marcozzi, Teresa Panzera, Chiara Donfrancesco, Augusto Di Castelnuovo, Cinzia Lo Noce, Sara Magnacca, Federico Triolo, Maria Beatrice Zazzara and 5 more

Abstract read
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Article in Aging clinical and experimental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors.

Cecilia Damiano *Department of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy. cecilia.damiano@iss.it.ORCID http://orcid.org/0000-0001-5985-4164
Simona Costanzo *Research Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Benedetta MarcozziDepartment of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Teresa PanzeraResearch Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Chiara DonfrancescoDepartment of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Augusto Di CastelnuovoResearch Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Cinzia Lo NoceDepartment of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Sara MagnaccaResearch Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Federico TrioloAging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Maria Beatrice ZazzaraFondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Luigi PalmieriDepartment of Cardiovascular, Endocrine-Metabolic Diseases and Aging, Istituto Superiore di Sanità, Rome, Italy.
Licia IacovielloResearch Unit of Epidemiology and Prevention, IRCCS Neuromed, Pozzilli, Italy.
Graziano Onder *Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
Davide Liborio Vetrano *Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
BIO-SIGN Study Investigators

Funding

European Union- Next Generation EU- PNRR M6C2 PNRR-MAD-2022-12376569; cup C52C22001120007The Swedish Research Council 2021-03324
6 · The paper itself

Abstract

backgroundDifferent multimorbidity patterns can affect health trajectories and influence survival.

aimsWe investigated their association with mortality in two population-based cohorts of older adults.

methodsTwo Italian cohorts of randomly selected individuals (60–79 years old) from general population: CUORE (baseline 2008–2012) and Moli-sani (baseline 2005–2010). Latent Class Analysis used to identify homogeneous groups of multimorbid individuals (≥ 2 diseases) with similar underlying disease patterns. Cox regression models used to assess the association of multimorbidity patterns and all-cause mortality (end of follow-up 12/31/2019). Results pooled in a random-effects meta-analysis.

resultsTotal samples of 3,695 individuals in CUORE (48% male, mean age 68.8 years [SD 5.6]) and 7,801 in Moli-sani (51% male, mean age 68.2 years [SD 5.4]). In both cohorts, six multimorbidity patterns were identified and named after their overexpressed diseases: hypercholesterolemia; metabolic, depression and cancer; cardiometabolic and respiratory; gastrointestinal, genitourinary and depression; respiratory; unspecific (i.e., no diseases overexpressed). Overall mortality rates were 1.66 per 100 person/years in CUORE and 1.85 per 100 person/years in Moli-sani. Compared to the multimorbidity-free group (< 2 diseases), individuals displaying a cardiometabolic and respiratory pattern showed the highest mortality (pooled HR 2.62, 95% CI 2.15–3.10), followed by unspecific (pooled HR 1.45, 95% CI 1.21–1.68), respiratory (pooled HR 1.33, 95% CI 1.01–1.64) and gastrointestinal, genitourinary and depression (pooled HR 1.33, 95% CI 1.06–1.60). DISCUSSION: Multimorbidity patterns in older adults are differentially associated to shorter survival.

conclusionsTheir identification may help optimize clinical management by improving risk stratification, allowing for more targeted prevention and intervention strategies.

Indexed as

MortalityMultimorbidityAgedCohort StudiesDepressionFemaleHumansItalyMaleMiddle AgedNeoplasmsProportional Hazards ModelsChronic diseasePersonalized medicinePopulation-based studySurvival

Identifiers

PMID40833558
PMCPMC12367807

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.